Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got
Speaker 1: something a little bit different to share with you. It's
Speaker 1: a new season of the Smart Talks with IBM podcast series.
Speaker 2: This season on Smart Talks with IBM, Malcolm Gladwell is back,
Speaker 2: and this time he's taking the show on the road.
Speaker 2: Malcolm is stepping outside the studio to explore how IBM
Speaker 2: clients are using artificial intelligence to solve real world challenges
Speaker 2: and transform the way they do business.
Speaker 1: From accelerating scientific breakthroughs to reimagining education. It's a fresh
Speaker 1: look at innovation in action, where big ideas meet cutting
Speaker 1: edge solutions.
Speaker 2: You'll hear from industry leaders, creative thinkers, and of course
Speaker 2: Malcolm Gladwell himself as he guides you through each story.
Speaker 1: New episodes of Smart Talks with IBM drop every month
Speaker 1: on the iHeartRadio app, Apple Podcasts, or wherever you get
Speaker 1: your podcasts. Learn more at IBM dot com slash smart Talks.
Speaker 1: This is a paid advertisement from IBM.
Speaker 3: Hello, this is Malcolm Gladwell and you're listening to Smart
Speaker 3: Talks with IBM. Every year, Tech Week brings thousands of
Speaker 3: people together to network and learn about what's emerging across
Speaker 3: the technology ecosystem, and at this year's conference in San Francisco,
Speaker 3: I had an amazing opportunity to sit down in front
Speaker 3: of a live audience with Jay Gambetta. Jay has been
Speaker 3: with IBM for years and was recently promoted to Director
Speaker 3: of Research. In this job, Jay has an important mission
Speaker 3: helping the company build the future of computing. In the
Speaker 3: last episode of smart Talks, I began to learn about
Speaker 3: quantum computing from IBM Chairman and CEO Arvind Krishna. But
Speaker 3: this conversation I had with Jay went even deeper and
Speaker 3: convinced me that the development of quantum isn't just a fun,
Speaker 3: exciting new paradigm of computing, it may be one of
Speaker 3: the most important scientific achievements of my lifetime. Jay, Good morning, morning,
Speaker 3: Welcome to Smart Talks with IBM. Thank you special live
Speaker 3: recording here for tech Week and congratulations. How long have
Speaker 3: you been Head of Research at IBM?
Speaker 4: Since October one? It's October tenth today, since nine days,
Speaker 4: nine days.
Speaker 3: Can you just talk a little about the position. This
Speaker 3: is one of the most important positions in research in
Speaker 3: the world.
Speaker 4: IBM research has been around for eighty years and it's
Speaker 4: done some tremendous technology, a lot of inventions and fundamentals
Speaker 4: for semiconductors, algorithms, AI. Yeah, I think if we look
Speaker 4: back to where a lot of the innovation and the
Speaker 4: technology of the world comes from, I think you can
Speaker 4: find Ibram's footprints on it, and you can find IBM research.
Speaker 4: So yeah, I'm very excited for the opportunity, but I'm
Speaker 4: also aware that there's big shoes to fill, and I'm
Speaker 4: looking forward to how we take IBM research forward. Obviously,
Speaker 4: I'm going to be bringing a lot of the quantum side,
Speaker 4: which we're going to talk about later. Beyond quantum, there's
Speaker 4: important work that needs to happen in AI hybrid cloud,
Speaker 4: and I think we're going to also enter into this
Speaker 4: new period of mathematics where we get to use quantum
Speaker 4: machines and also AI machines. And there's some really good
Speaker 4: hard mathematical questions to answer.
Speaker 3: How many people do you have working for you?
Speaker 4: I've been researchers in the three thousand researchers across many
Speaker 4: different labs around the world. Our main lab is in Yorktown,
Speaker 4: but then we have the lab actually out on the
Speaker 4: West coast in Armadan or SBL Now and then we
Speaker 4: have one in Zurich, Japan, and a few others around
Speaker 4: the world.
Speaker 3: Tell me a little bit before we get into quantum.
Speaker 3: I'm just curious about your path. So you're Australian. Yep,
Speaker 3: we were talking about earlier backstage. Your accent has become muted.
Speaker 3: You should crank it up because it's.
Speaker 4: Yeah, I'm slowly losing my Australian accent. I've been in
Speaker 4: the US since two thousand and four, so accent, you know,
Speaker 4: to sound very Australian. Yeah, but how do you practice it?
Speaker 4: Maybe I got to go back to Australia. Here a
Speaker 4: more Australians say gooday, how's it going?
Speaker 2: Like that?
Speaker 3: And you you didn't grow up thinking you're going to
Speaker 3: be a scientist one day, now.
Speaker 4: I grew up in a pretty normal life. My dreams
Speaker 4: as a kid was building things, so I was either
Speaker 4: going to be a carpenter or a mechanic. But I
Speaker 4: had some great teachers that inspired me to go to university.
Speaker 4: And I didn't even know, honestly what a scientist was.
Speaker 4: And then I found myself at university doing science, particular physics,
Speaker 4: and I ended up loving it.
Speaker 3: So you go from there to what do you do
Speaker 3: your PhD.
Speaker 4: So I did my undergrad in Australia. I did it
Speaker 4: actually in laser science, so I think I watched some
Speaker 4: TV show in lasers seemed interesting, so I wanted to
Speaker 4: learn about lasers. And then I realized in trying to
Speaker 4: understand lasers there was this quantum mechanics, and so I
Speaker 4: was like, all right, I want to actually understand this
Speaker 4: quantum mechanics. So I did my equivalent of what you
Speaker 4: and the US school masters. We call it honors in Australia,
Speaker 4: but we do a research project. I said, I wanted
Speaker 4: to shoot lasers into atoms and measure cross sections and
Speaker 4: I got really into quantum physics. So then I decided,
Speaker 4: all right, I don't understand this quantum physics. I want
Speaker 4: to do my PhD in interpretations of quantum mechanics. So
Speaker 4: I jumped in and said, all right, what is this
Speaker 4: quantum mechanics? Why is everyone arguing on these different interpretations.
Speaker 4: Then I finished my PhD in Australia doing that. Then
Speaker 4: I moved over at the end of my PhD interpretations,
Speaker 4: it's more people arguing about the equations whilst I think
Speaker 4: it's really important. I decided if it's going to be
Speaker 4: like a collapse equation versus many worlds, or a hidden
Speaker 4: variable model, or that just quantum mechanics decoheres because we
Speaker 4: don't see supersitions in the everyday world, because it interacts
Speaker 4: with environment. The only way to answer that question was
Speaker 4: to build a quantum computer. And so then I decided
Speaker 4: at the end of my PhD, I wanted to work
Speaker 4: out how to build a quant computer. And then I
Speaker 4: left there and I went to Yale, and then at Yale,
Speaker 4: that's where I got into superconducting cubits, which just a
Speaker 4: few days ago one of the professors there just won
Speaker 4: the Nobel Prize this year.
Speaker 3: Oh wow, Yeah, I'm very interested in tracing because your
Speaker 3: career follows the arc of quantum computing in a certain way.
Speaker 3: Right at the time when you asked the question, what
Speaker 3: I really want to do is to figure out how
Speaker 3: to build a quantum computer. Where are we in quantum
Speaker 3: computing at that point?
Speaker 4: Yeah, So that would have been nineteen ninety So there
Speaker 4: was Shaw's algorithm came out, let's say ninety five. There
Speaker 4: was a lot of theory. And then the reason I
Speaker 4: went to Yale is because people had started to show
Speaker 4: that they could see quantum effects in electrical circuits. So
Speaker 4: these macroscopic objects they were starting to behave quantum mechanical
Speaker 4: There was a really significant breakthrough in nineteen ninety nine
Speaker 4: where Yazoo Nakamura in Japan showed that a qubit could
Speaker 4: exist in these electrical circuits. And then I found out
Speaker 4: the group at Yale were really trying to take these
Speaker 4: electrical circuits and couple them together. And so it was like,
Speaker 4: if I can build something using electrical circuits and they're big,
Speaker 4: that that's the best way that you cancide to test
Speaker 4: and understand whether quantum mechanics breaks down at a macroscopic
Speaker 4: scale or not. Can we actually make them behave as cubits?
Speaker 4: And I agree. When I came to Yale, the cubits
Speaker 4: were not very good. They were actually a couple of nanoseconds.
Speaker 4: They were unstable. Electron would jump onto the chip and
Speaker 4: then they would change all their configurations, so you have
Speaker 4: to restart your experiment. And so for the first time
Speaker 4: at Yale, it's kind of what the challenge there was,
Speaker 4: how do we make a cubit? How do we make
Speaker 4: a stable cubit? And that took about five years and
Speaker 4: that took us up to two thousand and seven. And
Speaker 4: I think the rest of the world looks and says
Speaker 4: quantums like just blowing up. But it's actually been like
Speaker 4: all most phases theory, showing that we got the algorithms,
Speaker 4: how do we make a cubit? How do we couple
Speaker 4: of the cubits together? And now we're in the scaling phase.
Speaker 3: Describe for us, because many people in this room, me included,
Speaker 3: have only a kind of surface level understanding of what
Speaker 3: we mean when we use that phrase. What is the
Speaker 3: difference between classical computing and quantum computing? What does that
Speaker 3: word mean?
Speaker 4: Yeah, so you can go down the physics way and
Speaker 4: talk about supersition and entanglement, which we can go in later,
Speaker 4: but actually feel it's a bit of a distraction. So
Speaker 4: when you think of classical computers, what they were is
Speaker 4: there were machines that were very good at adding numbers together,
Speaker 4: like simple addition, and they really showed that they could
Speaker 4: add these numbers together really really fast. And now with
Speaker 4: GPUs and other AI accelerators, we can add those numbers
Speaker 4: together in parallel, and so the whole classical computing can
Speaker 4: come down to just arithmetic, just adding numbers together. It
Speaker 4: turns out that there's a math that is the quantum
Speaker 4: mechanics shown.
Speaker 3: To be true.
Speaker 4: It's more like a group theory type structure. And the
Speaker 4: way quantum works is it has a different mathos are primitive,
Speaker 4: and if we can exploit that new math and build
Speaker 4: a machine that does it, it allows us to answer
Speaker 4: different questions. And so think of it as a branching
Speaker 4: from classical compute that is very good at adding just
Speaker 4: numbers together to something that allows us to work with
Speaker 4: an algebra that is much much harder to represent with addition.
Speaker 4: And that algebra happens to be the same algebra that
Speaker 4: defines the fundamental equations of nature shirting as equation. So
Speaker 4: this is why you say it computes the same way
Speaker 4: nature does. But there are many other interesting problems. So
Speaker 4: the way I explain it to people is think of
Speaker 4: it as bringing a new primitive to computer science and
Speaker 4: allowing us to work how to go with it. And
Speaker 4: I like the analogy. Well, actually, maybe go back. So
Speaker 4: if you went back in time, so we're one hundred
Speaker 4: years of quantum, and you went back in time and
Speaker 4: you asked, what is the foundation? Is a chemistry or physics.
Speaker 4: What would have probably the scientists of one hundred years
Speaker 4: ago would have said is they would have said, you know,
Speaker 4: chemistry is about the small, physics is about planets and
Speaker 4: things like this. And one hundred years ago when Heisenberg
Speaker 4: or Einstein, all the greats, Schrodinger himself invented quantum mechanics,
Speaker 4: it was this concept that nature is discreete not continuous.
Speaker 4: It actually brought all the physical sciences together. And now
Speaker 4: quantum mechanics is like it is the foundation of the science.
Speaker 4: And so now what quantum computing is by that analogy
Speaker 4: is computer science. The foundation of the math is coming
Speaker 4: together with the physical science to allow us to compute
Speaker 4: using math that if you were to try to represent
Speaker 4: it with classical computers, it takes exponential time.
Speaker 3: Yeah, it was a classical computer and an expense in
Speaker 3: a way that someone is well informed as I am
Speaker 3: can understand it. A customer computer works primarily on problems
Speaker 3: that can be easily represented in numerical form in numbers. Yes,
Speaker 3: quantum allows you to step outside to a class of
Speaker 3: problems that don't necessarily have a simple numerical representation.
Speaker 4: Yeah, and so imagine I got some medicine or or
Speaker 4: some set of operation, but call it A and I
Speaker 4: then follow it by a different operation B if A
Speaker 4: followed by B gave a different answer than B first
Speaker 4: followed by A. So in mathematics we call that commuting.
Speaker 4: But like you can think of a correlation there one
Speaker 4: one gives you a different outcome to the other. That
Speaker 4: means there's an algebra behind it. That Representing that algebra
Speaker 4: traditionally on classical computers is really really hard, whereas that algebra,
Speaker 4: if we can get creative, we can come up with
Speaker 4: ways of representing that math. So we step as you say,
Speaker 4: we step out aside of the simple math to a
Speaker 4: new math to allow us to calculate interesting problems.
Speaker 3: So quite in a sense, compliments it doesn't replace judicial.
Speaker 3: That's good.
Speaker 4: I think this is one of the this is you're
Speaker 4: exactly on is people think quantum is going to be
Speaker 4: replacing classical If your problem is good at adding numbers together,
Speaker 4: you should just keep using classical computers. I think the
Speaker 4: future is going to be heterogeneous accelerators, and it will
Speaker 4: definitely have quantum as one. But in some sense, the
Speaker 4: next generation of superstars are going to be those applied
Speaker 4: mathematicians that know, how do I write a problem using
Speaker 4: the simple math of classical computers or the more complicated
Speaker 4: math for quantum computers, and how do I actually iterate
Speaker 4: between them? And things like this? This is where I
Speaker 4: think the next generation of students are going to come
Speaker 4: up with much more novel ideas. I can give you
Speaker 4: examples of what we want to do on quantum, but like,
Speaker 4: you're giving them a fundamental, foundational new thing, and so
Speaker 4: I'm optimistic that will do much better jobs than my generation.
Speaker 3: Well, yeah, we're to get to some of the albums
Speaker 3: in a moment, but I wanted you to the most
Speaker 3: kind of down to you said, as a kid, you
Speaker 3: thought you might want to be a mechanic because you'd
Speaker 3: like to build things. Describe to me what it takes
Speaker 3: to build a quantum computer, Like, what are you doing
Speaker 3: that's different from building a classical computer.
Speaker 4: Yeah, so maybe I'll give you analogy and then i'll
Speaker 4: go in so the way classical computers, we've got them
Speaker 4: to get to smaller and smaller sizes like five seven animeters,
Speaker 4: five animeters and things is actually inventing material to kill
Speaker 4: quantum effects, so you actually put dielectrics and other things
Speaker 4: in there. To kill the quantum tunneling effects, and you
Speaker 4: want them to behave more classically. In the quantum world,
Speaker 4: you want to get rid of all the classical effects,
Speaker 4: So you want to get rid of the ability of
Speaker 4: the cubits to interact with the environment. And in the
Speaker 4: in the sort of technical world, we call it this
Speaker 4: quantum conflict. The more ways you want to control the
Speaker 4: quantum computer, you open it up to interacting with everything else,
Speaker 4: like interacting with its environment. So the biggest challenge has
Speaker 4: always been how do we give more control but don't
Speaker 4: bring in other sources of noise. So I want to
Speaker 4: be able to do gates on the cubit, but I
Speaker 4: don't want it to decohere. I want to couple the cubits,
Speaker 4: but I don't want them to couple to other things.
Speaker 4: So the hardest challenge is the energy inside the cubits
Speaker 4: is a nine gigahertz, and if your tames that by
Speaker 4: HBO tend to the neggave thirty four with nine, you're
Speaker 4: at a tender the negative twenty like three or something
Speaker 4: in energy. That's a tiny amount of energy. So you're
Speaker 4: trying to have a tiny, tiny amount of energy to control,
Speaker 4: and you don't want that to interact with anything, So
Speaker 4: you have to cool them down, you have to isolate them,
Speaker 4: and you have to make the quantum effects dominate over
Speaker 4: the classical effects.
Speaker 3: So practically, if I'm trying to do that right now,
Speaker 3: how big are these machines.
Speaker 4: So the cubits themselves are not that big. So the
Speaker 4: cubits themselves are like a few microns. But yeah, most
Speaker 4: of the size so you can see some of our
Speaker 4: I got the pleasure of showing you around to one
Speaker 4: of the machines in Yorktown. You saw that they're like
Speaker 4: twenty foot by twenty foot in size. Most of that
Speaker 4: is all that equipment to isolate the cubit chip, which
Speaker 4: is only a few millimeters when you put it together
Speaker 4: from the rest of the environment. We will, as we
Speaker 4: get better at that, miniaturize all the isolation. But that's
Speaker 4: cooling it down to a few milli calvin, so about
Speaker 4: a thousand times colder than outer space. It's isolating the
Speaker 4: noise on any electrical signal so that no noise from
Speaker 4: the outside world gets into the system. And so that's
Speaker 4: a lot of isolators, filters, and things like that that
Speaker 4: we've had to invent and to allow us to make
Speaker 4: the quantum properties of this chip go.
Speaker 3: It's like the Princess and the pe mounds and mounds
Speaker 3: and mounds of mattresses trying to isolate the impact of
Speaker 3: this little thing, and that maybe.
Speaker 4: That's the best way to describe it. Yeah, and you've
Speaker 4: got to keep it really really prestige.
Speaker 3: But that when you show me. So in the in
Speaker 3: the lobby of the Watson Research Center in New Yorktown,
Speaker 3: which by the way, is just the coolest building. It's
Speaker 3: like a it's like a modernist it's also master piece. Anyway,
Speaker 3: in the lobby there's there are these is it two machines.
Speaker 4: It's it's inside a container that has three machines machines.
Speaker 3: So what can you can you tell me what would
Speaker 3: one of those machines cost to build right now?
Speaker 4: So typically we put them together in a way where
Speaker 4: we upgrade them because we want to as I as
Speaker 4: I was talking about before, one of the things we
Speaker 4: want to do is always get algorithms done on our machines.
Speaker 4: And I've got a roadmap of build bigger and bigger machines.
Speaker 4: So usually one of those quantum processes today is out
Speaker 4: of date in six months so we want to build
Speaker 4: this future of computing that leverages quantum computing, where every
Speaker 4: six months we've outdated a quantum processor. Eventually, hopefully we
Speaker 4: get to a point where it's like stable and it
Speaker 4: can be many years operating. But we want to get
Speaker 4: as large a quantum computer in the hands of people
Speaker 4: to explore the math as possible to come up with
Speaker 4: those new algorithms. So we've had a philosophy of having
Speaker 4: them open, working with universities and things like that. So
Speaker 4: to answer a question of costs, yes, there's cost in
Speaker 4: building the system, but we are operating in them much
Speaker 4: more in a service model where people pay to use
Speaker 4: the machine because we have to continuously calibrate it and
Speaker 4: operate it and so depending on various different things. Professors,
Speaker 4: we have a credits program where they get free access
Speaker 4: some universities and animal prizes. They can buy premium access
Speaker 4: and get more access. So think of not like a
Speaker 4: cost of it, because it's almost like a continuum. I
Speaker 4: want to make sure that the best quantum processors that
Speaker 4: I can build get in the hands of students and
Speaker 4: professors and interested enterprises that want to explore these machines
Speaker 4: as fast as possible, and typically every six months we
Speaker 4: upgrade it.
Speaker 3: Yeah, you don't start over, you upgrade.
Speaker 4: We upgrade various different pieces, the processor, the electronics. Some
Speaker 4: upgrades are just simply replaced the processor. But as an example,
Speaker 4: I think many people have probably seen photos of quantum
Speaker 4: computers and you see this scary thing with all these
Speaker 4: wires hanging down, as I've referred to as the chandelier,
Speaker 4: and it's got all these wires with loops and things
Speaker 4: like that. They're called co x keebles. When we first
Speaker 4: put the quantum computer on the cloud in twenty sixteen,
Speaker 4: you could probably only fit about fifty cubits inside one cryostat.
Speaker 4: We've had to upgrade all those cables so that we
Speaker 4: can fit around one thousand. I want to get to
Speaker 4: three thousand, and that's about miniaturizing it. So to answer
Speaker 4: your question, an upgrade, it depends. It can be either
Speaker 4: just the processor or it can be the complete insides.
Speaker 4: And we're actually in our third generation of our electronics
Speaker 4: to control the systems, to make them faster, less noise. Internally,
Speaker 4: we've got exciting results of going to something like cold cryocemos,
Speaker 4: so you can bring down the cost in terms of
Speaker 4: energy of running these quantum computers almost negligible, and you
Speaker 4: could imagine future quantum computers. I'm not going to require
Speaker 4: much energy to run, so unlike classical compute that requires
Speaker 4: lots of energy. The biggest machines that we envision is
Speaker 4: only in the few megawatts. But we have to upgrade
Speaker 4: to future controls that use less energy. So it depends.
Speaker 4: It's my long answer, short answer to how it upgrades,
Speaker 4: and it depends on what it is.
Speaker 3: The only observation that I felt I could with making
Speaker 3: when you showed me the quantum machine is it's gorgeous.
Speaker 3: I look at art.
Speaker 4: I've always believed that, and I think that there's an
Speaker 4: IBM saying good design is good business. But we've always
Speaker 4: taken pride in making sure what we build. I don't know.
Speaker 4: I feel if you're going to build something that is
Speaker 4: new that can change, you should take the time to
Speaker 4: make sure it looks and feels good.
Speaker 3: Will you donated to MoMA when you're through with that
Speaker 3: particular Actually, I think we just put.
Speaker 4: An old version of one of our insights with the
Speaker 4: United Airlines and the AAPS, which is the American Physical Society,
Speaker 4: and the University of Chicago. There's a replica right now.
Speaker 4: If you fly into one of the terminals in Chicago,
Speaker 4: you can walk and see one.
Speaker 3: Oh really yeah, well the most advanced thing at all air.
Speaker 4: I'm sure probably, but yeah, I hopefully, I think, yeah,
Speaker 4: we're open to that. But yeah, I appreciate that you
Speaker 4: love the design. It was beautiful.
Speaker 3: So I last week I interviewed for another episode of
Speaker 3: Smart TALX your CEO, Ivin Kushner, And when we got
Speaker 3: to the quantum question, I mean, he's always alliant and brilliant,
Speaker 3: but quantum, he's like lit up. I mean right in
Speaker 3: thinking that IBM is much more invested in quantum than
Speaker 3: anybody else. Is that a fair statement? Oh yeah, most definitely.
Speaker 3: Why Why did IBM choose to kind of make this
Speaker 3: such a priority.
Speaker 4: So when I took to the history of the physics side,
Speaker 4: there's this interesting thing in the history of computing. So
Speaker 4: we build computer classical computers today using bits and sea moss,
Speaker 4: and they consume energy. Do you know that there is
Speaker 4: a way in classical where you can actually compute without
Speaker 4: using energy. It's called reversal computing. Turns out to be
Speaker 4: a terrible idea. It's not practical to build. But IBM
Speaker 4: investigated that with Ralph Laura and Charlie ban It early
Speaker 4: on and they proved the concept that reversible computing. The
Speaker 4: first use of quantum information theory, one of the first
Speaker 4: actually was from IBM. When I did my PhD, I
Speaker 4: remember actually picking up this paper on quantum teleportation and
Speaker 4: seeing IBM written there, and at the time I remember
Speaker 4: thinking that they make PCs. Well, what the hell are
Speaker 4: they doing this foundational paper on quantum teleportation? Why are
Speaker 4: they doing it? So to answer your question, actually, IBM
Speaker 4: was the first in quantum information science because it's the
Speaker 4: fundamental of computation. Can we actually come up with compute
Speaker 4: that we can go beyond the classical So way before
Speaker 4: anyone was talking about it, they were doing fundamental theory.
Speaker 4: And then as we've built it, we've always When I
Speaker 4: first came there, the experimental team was small. In twenty eleven,
Speaker 4: we've had a small team that we're focusing on single
Speaker 4: cubitts coupling them. I think in two thy twelve was
Speaker 4: the first time we showed really good two Cuba gates
Speaker 4: and no one was talking about quantum computing then. And
Speaker 4: then I remember in about twenty sixteen, I said to
Speaker 4: actually Arvin was the director of research, then can we
Speaker 4: actually put our quantum computer on the cloud? Well that's
Speaker 4: probably twenty fifteen, and it was always supporting that. So
Speaker 4: as we've done more and more we've been able to
Speaker 4: do it. It's had this program going now, I agree
Speaker 4: is very visible, like because we're in this scaling phase
Speaker 4: and so we're invested to keep scaling it and to
Speaker 4: get why is At IBM research, what we always do
Speaker 4: is answer what is the future of computing? Whether it's
Speaker 4: coming up with new algorithms, coming up with better AI,
Speaker 4: coming up with quantum, or coming up with just how
Speaker 4: do different accelerators go together. It's our DNA to answer
Speaker 4: the question of what is the future?
Speaker 3: Need a perfect problem for IBM because you kind of
Speaker 3: need to have a legacy of building stuff, building actual
Speaker 3: physical machines.
Speaker 4: Yeah, that's why I came to IBM. I wanted the experience,
Speaker 4: the culture of building hard things that others have not
Speaker 4: done before.
Speaker 3: Where do you imagine we are in the timeline of
Speaker 3: this technology? It will come a point when it will mature.
Speaker 3: My cell phone is a mature technology at this point.
Speaker 3: How far are we from that point? With condom.
Speaker 4: So I think there's various aspects of it. So we
Speaker 4: sat in twenty and seventy we set our goal that
Speaker 4: in twenty twenty three we would be able to build
Speaker 4: a machine that was beyond classical computers to simulate it.
Speaker 4: And we achieved that in twenty twenty three. So to
Speaker 4: run a biggo we call it a quantum circuit. The
Speaker 4: details of it don't matter, but to run a quantum
Speaker 4: workload that if you were to simulate that workload how
Speaker 4: a quantum computer operates, it's on a classical computer, you
Speaker 4: couldn't do it. So we said that as our first
Speaker 4: and now I've made it publicly that by twenty twenty
Speaker 4: nine we'll build the first fault tolerant quantum computer. That is,
Speaker 4: one that can completely handle the noise to the level
Speaker 4: to allow you to run a very very large, large problem.
Speaker 3: So an example of a large problem.
Speaker 4: Yeah, a large quantum problem. So for around a couple
Speaker 4: of one hundred cubits and one hundred million operations, you're
Speaker 4: talking still interesting science problems like simulating a molecule, or
Speaker 4: calculating a small optimization problem, or calculating, say some part
Speaker 4: of a matrix update. In some type of differential so
Speaker 4: it'll still be scientific, but it'll be at the point
Speaker 4: where it's beyond, well beyond any classical approximate method. And
Speaker 4: then I think that's twenty twenty nine. That's twenty twenty nine,
Speaker 4: So we're four.
Speaker 3: Years away from something that can start to handle.
Speaker 4: Interesting problem, serious problems. I do believe the scientists will
Speaker 4: find interesting heuristic problems before that, And so over the
Speaker 4: next four years, you're going to continue to see more
Speaker 4: and more let's call them heuristic not provable quantum problems
Speaker 4: that run on quantum computers that come out. We're see
Speaker 4: more and more come from many of our partners and ourselves.
Speaker 4: Heuristic problems have value, but they have to be tested,
Speaker 4: they have to stand up over time. You have to
Speaker 4: run them many, many times, you have to try different ones,
Speaker 4: and many times heuristic can lead to formal problems. So
Speaker 4: you're going to see, because we're beyond now the point
Speaker 4: that you can simulate these quantum computers with any classical computer.
Speaker 4: They're kind of like a scientific tool. So they're exploring
Speaker 4: the heuristic.
Speaker 3: What do you have to get done between now and
Speaker 3: twenty twenty nine to get there?
Speaker 4: So we had to reinvent how we wanted to do
Speaker 4: error correction. So we have to demonstrate modules and if
Speaker 4: we can demonstrate these error corrected module and our goal
Speaker 4: is actually it's called Kooko, but I name all that
Speaker 4: chips after birds, so it's called Kuoko. Borrow is named
Speaker 4: after an Australian vert. I think I still say Cuoko
Speaker 4: Borrow the way Australians do. We need to then show
Speaker 4: that we can make a single module and then we
Speaker 4: want to connect two of those modules together, and I
Speaker 4: call that one Cockatoo, which is another Australian vert. And
Speaker 4: then if we can do that, so that's twenty six
Speaker 4: and twenty seven, and then we want to scale them
Speaker 4: scale those modules, and that we call Starling, and we
Speaker 4: want to scale that in twenty twenty nine. So get
Speaker 4: a module, join two modules together, and scale and so
Speaker 4: each module is going to be around one thousand cubits.
Speaker 3: The challenge to getting there is it finding the right
Speaker 3: material or how would you describe what that's.
Speaker 4: The beauty to be done. That's the beauty of it
Speaker 4: is if we would have been here two years ago,
Speaker 4: I couldn't tell you how it would be done. So
Speaker 4: we had a huge breakthrough we came up with a
Speaker 4: new code, a new quantumeric Russian code, and that code.
Speaker 4: The biggest in part of that code that is the
Speaker 4: most important is it's modular in nature. So previous codes,
Speaker 4: without getting too technical, they were very monolithic and you
Speaker 4: had to build a very big device and I wouldn't
Speaker 4: have known we would have to invent tools like new
Speaker 4: simos tools to do that. So we came up with
Speaker 4: this new code. We started on twenty nineteen, we published
Speaker 4: in twenty twenty four. We kind of had most of
Speaker 4: things worked out in twenty twenty three. That's why we
Speaker 4: got confident to release the thing. So the biggest breakthrough
Speaker 4: we had is coming up with a code that's modular
Speaker 4: in nature. And think of that as a like a blueprint.
Speaker 4: And so now we have the blueprint and now we're
Speaker 4: doing engineering tasks to implement every part of that blueprint.
Speaker 3: And so the minute you had that breakthrough, then you
Speaker 3: began to have confidence at something exactly these goals could
Speaker 3: be met.
Speaker 4: And then you can't. And then anyone that's done engineering
Speaker 4: will know what I'm talking about when I say this
Speaker 4: is cycles are learning. It takes so long from test
Speaker 4: idea to build two tests in hardware, the cycles of
Speaker 4: learning are much much lower than software, Like you can
Speaker 4: be really, really faster in the software. So then we've
Speaker 4: planned out our iterations over the next few years, and
Speaker 4: so we have to successfully demonstrate them. I may slip
Speaker 4: because sometimes you may estimate your time wrong, but we
Speaker 4: now have exactly what we want to do for the
Speaker 4: next four years.
Speaker 3: I want to go back to that breakthrough for a moment.
Speaker 3: What does the word breaks we mean in that context, Like,
Speaker 3: it's not that you get a call in the morning
Speaker 3: from somebody who says.
Speaker 2: I did it?
Speaker 3: Do you see it coming? Or is it a surprise
Speaker 3: when they get there.
Speaker 4: So the way this one worked is Sogo Brave, who's
Speaker 4: an algorithm person at IBM, one of the smartest and
Speaker 4: quantum information.
Speaker 3: Don't mention his name to everyone. You'll come for him.
Speaker 4: Everyone in quantum already knows his name. I don't think
Speaker 4: there's an idea that has not originated from him in quantum.
Speaker 4: So we're looking at other codes and we'll go all right,
Speaker 4: we've got to get serious about these codes. And others
Speaker 4: were starting to propose to bring these and then we
Speaker 4: call them LDPC codes from the classical space into the quantum,
Speaker 4: and I asked him, we need to get ahead of
Speaker 4: this and understand what they're doing it. He's like, the
Speaker 4: most modest perfuse late, Jay, let me learn about them
Speaker 4: and I'll generate a report for us and we'll read
Speaker 4: through it. And then I said great. Then I don't know.
Speaker 4: Six months later, he comes back with one hundred page
Speaker 4: report on everyone everyone had done in LTPC codes. I'm like, awesome.
Speaker 4: So I started then to read from them. And then
Speaker 4: we said, all right, how do we under the assumptions
Speaker 4: of the hardware we can build? Can we get an
Speaker 4: LTPC code knowing what we can build? And that's a
Speaker 4: great question, and so we put a small team together
Speaker 4: to investigate and honestly took two to three years, and
Speaker 4: we iterated and we used the constraints, so we had
Speaker 4: the sort of theory, and then we had the constraints
Speaker 4: of what we could build. And we iterated for a
Speaker 4: few years, and then at the end of that we
Speaker 4: came out with a solution that yes, it is possible
Speaker 4: to meet all the constraints of the hardware and build
Speaker 4: a code that will work.
Speaker 3: I'm just curious about So you had this task, this problem,
Speaker 3: you want to solve. And when you set out on
Speaker 3: the task of trying to solve the problem, what's your
Speaker 3: certainty level that you'll get a solution.
Speaker 4: Well, that's the beauty of science for things where you
Speaker 4: kind of have a few ideas. My philosophy is try
Speaker 4: a few for the ones that need to be in
Speaker 4: that like wow moment. It's honestly, you've got to set
Speaker 4: the ambition really, really high, but know when to stop.
Speaker 4: It was a great team that went together to get
Speaker 4: that breakthrough, and we knew that we needed to come
Speaker 4: up with a code that met the requirements of the experiment.
Speaker 4: And I think what was different before then is the
Speaker 4: theorists that were doing error correction codes didn't necessarily know
Speaker 4: the constraints of experiments, so it was like really more
Speaker 4: pen and paper. So this became one, all right, given
Speaker 4: these sets of constraints, is it possible when LA's.
Speaker 3: Questions about this? Sorry, I love these kind of moments
Speaker 3: when things become clear. At the time the problem was solved,
Speaker 3: were you aware of the implications of the solution or
Speaker 3: did that takes you knew exactly what.
Speaker 4: We set out exactly like either we were going to
Speaker 4: have to work out how to cool down a very
Speaker 4: large piece of silicon, which would require a lot of
Speaker 4: engineering and building tools beyond what anyone has ever built
Speaker 4: in the silicon semoss industry to implement the known codes,
Speaker 4: or we had to come up with a different one,
Speaker 4: and once I knew that we had one, that I
Speaker 4: didn't need to re invent any tools to build. The
Speaker 4: implications are clear how.
Speaker 3: Much time elapsed between the time you heard the problem
Speaker 3: was solved and the time you told Arvin Krishna, the CEO,
Speaker 3: the problem was solved.
Speaker 4: I'm sure the next time I spoke to him, I update,
Speaker 4: but I don't remember. The beauty of Avin is he
Speaker 4: trusts the scientists will do it, and so he doesn't
Speaker 4: really check on us. We update him when it is,
Speaker 4: and he he empowers us to do really hard problems.
Speaker 3: Yeah, so let's talk about uses. I mean, they're really
Speaker 3: like cool, big shiny machine. I think you'll get pay
Speaker 3: twenty twenty nine. But there's all kinds of really interesting
Speaker 3: problems you're already working on.
Speaker 4: Yes, this is like another interesting area is I can
Speaker 4: prove in pen and paper algorithms that we want to
Speaker 4: run that. Like, it's not that we don't know what
Speaker 4: to do with a quantum computer, there are hundreds of algorithms.
Speaker 4: So you can go to I think it's called quantumzoo
Speaker 4: dot com and you can see many many algorithms. People
Speaker 4: are coming up with more of more of them that
Speaker 4: they prove by pen and paper. Imagine, now we have
Speaker 4: a machine that you can't simulate. How do you actually
Speaker 4: discover algorithms in a scientific way? How do you look
Speaker 4: and discover algorithms using a quantum computer. We're in this
Speaker 4: exciting period right now, and so even though I can
Speaker 4: prove these ones that we can run in the future,
Speaker 4: there's a big white space between what the machines we
Speaker 4: have and we're going to build and continue to do
Speaker 4: and those ones that want the provable ones. And I'm
Speaker 4: an optimistic person by nature. I think getting those machines
Speaker 4: in the hands of students to explore and look at
Speaker 4: heuristic algorithms, so looking at the equivalent of doing numerical
Speaker 4: algorithms on computers, which there's many histories of numerical algorithms
Speaker 4: being discovered on classical computers before we had formal pre
Speaker 4: that we rely on today. People would even argue the
Speaker 4: way AI works was driven numerically, even though we have
Speaker 4: input into it. There are ones in optimization driven numerically.
Speaker 4: We are entering that phase. So the computer scientists now
Speaker 4: need to go play with these primitives. Our prediction is
Speaker 4: over the next couple of years we're going to see
Speaker 4: valuable numerical equivalent algorithms emerge. And where the scientists are
Speaker 4: going is in four categories. One is simulating nature, so
Speaker 4: looking at either Haanji physics, chemistry, light problems. As an example,
Speaker 4: with our partners in Japan, they took one of our
Speaker 4: quantum computers and for Gackle, a very large classical supercomputer,
Speaker 4: and they ran a problem where quantum was just a
Speaker 4: sub routine of the problem that was running on all
Speaker 4: of for garcule, and they were able to look at
Speaker 4: an interesting molecule, a molecule that if you would go
Speaker 4: by pen and paper you would have said to take
Speaker 4: me a very long time to run that. They were
Speaker 4: able to run that quite accurately, heuristically, and already get
Speaker 4: results that are comparable with the best classical methods. So
Speaker 4: they are extremely excited because they want to push that
Speaker 4: further and they're sort of showing that you can take
Speaker 4: a classical supercomputer with quantum as a subroutine and start
Speaker 4: to push the level they were.
Speaker 3: This was trying to solve a medical problem.
Speaker 4: Is this one is a like most people don't realize,
Speaker 4: Like iron sulfur, just something as simple as iron and sulfur,
Speaker 4: we can't solve that exactly, Like iron sulfur, molecules are
Speaker 4: too hard, so really small small molecules are really really hard,
Speaker 4: too hard for classical computers to solve. People think we
Speaker 4: can solve a lot of things. It actually turns out
Speaker 4: we can't solve very much.
Speaker 3: You say solve its instance, you know precisely how that
Speaker 3: molecule works and it's constructed.
Speaker 4: No precisely what the energy levels of that molecule is
Speaker 4: and how they come together, and then be able to
Speaker 4: do that on a classical computer and compare it talk.
Speaker 3: It would be really really useful to know that specifically, because.
Speaker 4: If you can have energy levels, then you can estimate
Speaker 4: reaction rates. If you can estimate reaction rates, you can
Speaker 4: see how different types of chemicals will react. That can
Speaker 4: then lead to better informing eventually how to build materials
Speaker 4: or even drug design. I just want to be careful
Speaker 4: and not say, oh, we're going to solve drug design
Speaker 4: or that because there's many scientific steps to make that so.
Speaker 4: And so what quantum gives you as a different tool
Speaker 4: to give you more accuracy and then lead to making
Speaker 4: the different methods work.
Speaker 3: You can subcontract out aspects of a problem quantum right now,
Speaker 3: and that just gets you further along than you would
Speaker 3: have been.
Speaker 4: So at the moment, even this result still does not
Speaker 4: beat the best approximate classical method. It's comparable. So the
Speaker 4: art of chemistry for the last hundred years has been
Speaker 4: about approximating. So what we've done is we have got
Speaker 4: very very good at coming up with ways of approximating nature.
Speaker 4: And a lot of the things that we do and
Speaker 4: we exploit and we use to estimate approximations. They don't
Speaker 4: a stimulate nature of the way nature is. They approximate it.
Speaker 4: And there's I could list many different acronyms of different
Speaker 4: methods that go into approximating nature. What quantum gives us
Speaker 4: is to eventually get beyond that approximation and do it
Speaker 4: the way nature works. And so we aren't beating those
Speaker 4: approximation methods, and this is why I think, this is
Speaker 4: why it's still in the science. But they're getting comparable,
Speaker 4: getting comparable with a new tool where the previous tool
Speaker 4: is a dead end makes scientists very excited. Yeah, that
Speaker 4: nuance is where it is, and so that's in machine learning.
Speaker 4: Sorry Hamiltonian. Then there's examples in differential equations, So can
Speaker 4: I actually come up with differential equations and solve them?
Speaker 4: And if I can solve them, you could look at
Speaker 4: things like an obvious Stokes goes into weather. There's financial
Speaker 4: differential equations that you can better predict. So differential equations,
Speaker 4: there's many different examples there. And then I would say
Speaker 4: that two others are optimization and then there's quantum versions
Speaker 4: of machine learning that are very exciting as well.
Speaker 3: Cleveland Clinic one of the organizations that you guys have
Speaker 3: worked with. Why would the Cleveland Clinic be calling you up?
Speaker 4: Because that problem that they want to look at. So
Speaker 4: they've also done similar problem to the recent lab. So
Speaker 4: they've taken that method now and they've looked at molecules
Speaker 4: that matter for drug design. So they're fundamentally looking at
Speaker 4: those molecules that matter for eventually replacing some of the steps.
Speaker 4: So they're investing to see how reliable it can be done.
Speaker 4: And so there's a scientist there that's done many iterations
Speaker 4: now using the techniques that were done first with the
Speaker 4: team in Japan, they've now replicated that for new molecules
Speaker 4: that are essential primitives for eventually designed drugs and things
Speaker 4: that may matter for medical Yeah.
Speaker 3: And also there's some finance firms yep, HBC, Van Good yep,
Speaker 3: and their interest is.
Speaker 4: What so that was the differential equation and optimization. So
Speaker 4: if you are doing very large calculations like risk portfolio,
Speaker 4: or if you want to model the black Shaw's equation
Speaker 4: or things like this that are fundamental for them to
Speaker 4: make better predictions, come up with better trades and things
Speaker 4: like this. That is a very hard computational task. And
Speaker 4: so rather than quantum replacing that whole problem, can quantum
Speaker 4: be a subroutine in there? And what HSBC showed is
Speaker 4: they showed they could take their real data, they could
Speaker 4: take their real classical method and they just replaced a
Speaker 4: tiny part of it. They replaced a tiny part of
Speaker 4: it with a quantum subroutine that allowed them to come
Speaker 4: up with better predictions of the weights that then when
Speaker 4: they were to compare trial A versus Trial B, it
Speaker 4: was thirty four percent better at algorithmic tun and that's
Speaker 4: a big deal for them.
Speaker 3: It's huge.
Speaker 4: Yes, Now do they need to do more trials? Do
Speaker 4: they need to see is this a heuristic algorithm? Do
Speaker 4: we need to be careful? Is there other classical algorithms
Speaker 4: that go into these are great questions that are now
Speaker 4: being investigated. So think of this period of heuristic algorithms
Speaker 4: is really a period of scientific discovery using these machines,
Speaker 4: knowing that we want to continue and build the ones
Speaker 4: which have determinist their algorithms that can run.
Speaker 3: Do the people who would profit the most space starting
Speaker 3: to run quantum experiments realize that they would profit so
Speaker 3: much from running quantum experience And does the world know this?
Speaker 3: You've given us a couple of specific examples, but generally speaking,
Speaker 3: there must be a very large universe of people who
Speaker 3: could gain from at least starting to play in the space.
Speaker 4: So the enterprises that use computation as key for their
Speaker 4: understand the limits of classical computation and they're very interested
Speaker 4: to get started. The universities are very interested. Could we
Speaker 4: get more students doing more algorithms one hundred percent. Some
Speaker 4: of the limitations on the rate of algorithm discovery is
Speaker 4: because people are thinking through the classical way of writing algorithms.
Speaker 4: My belief is yes, So this is why we want
Speaker 4: to get more and more students and things, because it's
Speaker 4: just starting. But I would say in general most people
Speaker 4: are aware of it. Could we get more, could we
Speaker 4: accelerate it?
Speaker 3: Yes?
Speaker 4: Do we need to make better hardware, do we need
Speaker 4: to come up with better libraries, yes? Do we need
Speaker 4: better software yes, But it's all happening over the next
Speaker 4: few years.
Speaker 3: Is it hard to get someone who's spent their entire
Speaker 3: life thinking in terms of solving problems to classical means
Speaker 3: to make the transition to this new paradigm.
Speaker 4: There's a lot of examples when you approach something with
Speaker 4: the classical intuition, it's not the right way to do
Speaker 4: it when you approach it through the quantum. But if
Speaker 4: people are being taught to understand the fundamentals of the math,
Speaker 4: then a lot of the techniques carry across. I don't
Speaker 4: recommend people need to learn about entanglement or supersition because
Speaker 4: whilst the physicists will argue like spooky action a distance
Speaker 4: and all these type of things, entanglement is the power. Yes,
Speaker 4: that's how physicists are labeled. How quantum is different. But
Speaker 4: I would say, do we need some physicists really worrying
Speaker 4: thinking about that? Yes, but we need more applied mathematicians
Speaker 4: that are realizing they can use this as a as
Speaker 4: a different way of looking at the problems.
Speaker 3: Yeah. And when I asked you one question, know is
Speaker 3: we're describing a a It's more than a new technology.
Speaker 3: We're talking about a new paradigm. It's a way of
Speaker 3: thinking about problems. Can you compare this to kind of
Speaker 3: previous technological paradigms. I'm thinking at the last couple hundred years,
Speaker 3: what does this rank in terms of a new field
Speaker 3: that we've opened up.
Speaker 4: It's a hard question to answer, but I often say
Speaker 4: the history of computing, this will be the first time
Speaker 4: that computation is branched between classical and quantum. I like
Speaker 4: thinking reading a lot in the past. One of the
Speaker 4: things that I think was a way we changed as
Speaker 4: a society was the invention of zero. Before zero, math
Speaker 4: was limited. Realizing that numbers have a number a zero
Speaker 4: allowed us to develop a whole set of new mathematics
Speaker 4: that then went on and defined like everything from waves
Speaker 4: to calculus to all of that. Yes, we can describe
Speaker 4: it with that same math, but when we describe it
Speaker 4: with that math, it gets exponentially big and gets impractical
Speaker 4: to do. Now we can actually work on it, I
Speaker 4: would say, if I had to give you a quick answer,
Speaker 4: maybe going all the way back to when we were accepted.
Speaker 3: Zero, I thought you were going to say, like the airplane,
Speaker 3: but in fact, yeah, you went several orders of magnitude
Speaker 3: on that.
Speaker 4: Yes, but but I think it's sort of fundamental.
Speaker 3: This is absolutely fascinating. Thank you so much for chatting
Speaker 3: with me about it.
Speaker 4: Thank you for your time.
Speaker 3: Hey, listeners. So normally we end this episode here, but
Speaker 3: the Tech Week attendees asked Jay some really great questions,
Speaker 3: questions I wish I'd asked, so we wanted to include
Speaker 3: those here. Enjoy.
Speaker 5: Hi, J, thank you so much for the great presentation.
Speaker 5: My name is Trixie Apiado. I work for Willis Towers Watson,
Speaker 5: an insurance broker. I help seisos identify and quantify their
Speaker 5: cyber risk so they can prepare for threats before they happen,
Speaker 5: and so quantum threats keep me up at night. You
Speaker 5: mentioned so many good problems that quantum can solve. It
Speaker 5: can also break encryptions in our classical computer systems. So
Speaker 5: what safeguards or policies do you implement in your teams
Speaker 5: to build quantum capabilities responsibly? And what can we do
Speaker 5: for people in this room as builders and users to
Speaker 5: secure our data in systems before quantum computers become more
Speaker 5: energy efficient, cheaper, and more available.
Speaker 4: So it's a great question. So yes, one of the
Speaker 4: algorithms for quantum computing is to break our traditional encryption.
Speaker 4: So at IBM Research, we were aware of this from
Speaker 4: day one. We've come up with algorithms that we believe
Speaker 4: and have very strong evidence will not be broken by
Speaker 4: a quantum or classical computer, and this has selected them.
Speaker 4: So first the scientific technical question, security is saved. There
Speaker 4: are algorithms that exist that we can implement that neither
Speaker 4: a quantum or classical computer can break. So the technical
Speaker 4: answer is we're all okay. The more complicated answer is
Speaker 4: a social and society answer. Encryption was built in classical
Speaker 4: computing in a way that was never thought of being upgraded.
Speaker 4: It's mixed everywhere. Some of it is downstream, some of
Speaker 4: it is like software that you may use. Some of
Speaker 4: it is software that you've developed. And I get that
Speaker 4: if you've got a product and you want to have
Speaker 4: it secure for the next ten years, you probably want
Speaker 4: to think about how you're going to upgrade it, or
Speaker 4: if you have data that needs to be secure for
Speaker 4: the next ten years, it needs to upgrade to new encryption.
Speaker 4: So the real challenge is more of a social business
Speaker 4: problem of how do we actually transition from old encryption
Speaker 4: to new encryption knowing this is going to happen. So
Speaker 4: we at IBM have been very proactive on this. We've
Speaker 4: developed tools where we can determine where encryption is used,
Speaker 4: We've developed tools which can show you how to replace it,
Speaker 4: and we early on have made sure the Mainframe when
Speaker 4: we made these algorithms. So I think it was Z
Speaker 4: sixteen that was the first version of the Mainframe to
Speaker 4: have these quantum safe algorithms implemented. So my answer to
Speaker 4: your question is, yes, there's a real problem, but it's
Speaker 4: not a technical problem. It's a social and business problem.
Speaker 4: And I'm not minimizing that. I understand that that is
Speaker 4: a lot of work you need to start now. You
Speaker 4: need to come up and do a you need to
Speaker 4: make it part of your IT transformation. You need to
Speaker 4: get onto it. And I realize I realize it's not
Speaker 4: going to take zero time because it's not an easy
Speaker 4: problem to do. So the short answer is one we
Speaker 4: developed algorithms that we can't and we're developing tools to
Speaker 4: help you in that transformation.
Speaker 6: Thank you so much, Thank you. My name is Emma.
Speaker 6: I'm a product manager at Expedia working on software side
Speaker 6: of things. My question is around the non technical roles
Speaker 6: outside of the researchers, the mathematicians, the builders. How can
Speaker 6: the rest of us, whether it be policymakers, those in
Speaker 6: the legal fields, those thinking about what use cases quantum
Speaker 6: can solve for in the future, what should we be
Speaker 6: thinking about and how can we prepare for that.
Speaker 4: It's a good question. I think this is part of
Speaker 4: the requirement of the scientists to being able to articulate
Speaker 4: where they are. We need a forum for those type
Speaker 4: of discussions. I think a lot of this can fit
Speaker 4: within the forums that we already have for classical and AI,
Speaker 4: and I think we need to just be asking how
Speaker 4: do we actually bring them into them. Because I don't
Speaker 4: think of quantum as a replacement of compute. I think
Speaker 4: of it as an accelerator that expands what is possible,
Speaker 4: and I think we can ask those questions in those forums,
Speaker 4: are we doing enough now? I think I agree with you. No,
Speaker 4: I don't know the answer to it.
Speaker 6: I think it's a really interesting perspective because those existing
Speaker 6: forums do start to bring in those other fields as well,
Speaker 6: so it could warrant the same sort of discussion and.
Speaker 4: Active And I understand those forums right now AI is
Speaker 4: probably dominating, and it should be like we are going
Speaker 4: through a period of time where AI is impacting society.
Speaker 4: The technology is impacting society in big ways. So I
Speaker 4: totally understand that most of their focus should be on AI,
Speaker 4: but we should start to ask where is quantum in
Speaker 4: that as well?
Speaker 7: Hi, I'm Gobi and I'm a graduating PhD student at
Speaker 7: Northwestern and also a member of south Park Commons, which
Speaker 7: is a fund here. You mentioned earlier that some problems
Speaker 7: are best solved by classical versus some problems are best
Speaker 7: solved by quantum. When we're thinking about this, if we're
Speaker 7: not experts in quantum, but we're thinking about this from
Speaker 7: an AI perspective, could you just clarify when we think
Speaker 7: about quantum, what is deterministic and what is not deterministic.
Speaker 4: I think the future of computing we've got to get
Speaker 4: our heads around is that not everything is deterministic, and
Speaker 4: it's much more going to be probilistic. How do you
Speaker 4: handle error bars? How do you put confidence? I think
Speaker 4: a lot of those questions which you're referring to in
Speaker 4: AI are going to completely apply in quantum. I actually
Speaker 4: think it's a mistake to compare AI verse quantum. I
Speaker 4: actually think of quantum as much its quantum verse classical compute,
Speaker 4: and AI is going to come across on top. So
Speaker 4: as we go forward and we get a better understanding,
Speaker 4: I'm not going to say quantum is going to replace
Speaker 4: the classical compute that enables AI, but I think some
Speaker 4: of the math you do in AI will be able
Speaker 4: to go to both. So what can we formally prove?
Speaker 4: I can come up with a problem where I take
Speaker 4: a circle and I color half of it red and
Speaker 4: half of it of blue, and then I say I'm going
Speaker 4: to apply an operation that takes those dots make it. Say,
Speaker 4: let's say ten dots over here red, ten dots over
Speaker 4: here blue, and I'm going to wind them around many
Speaker 4: many times. I can then show you that if you
Speaker 4: feed that into a classical computer, it's a classical random
Speaker 4: number generator. You can give your as much data as
Speaker 4: you want, you will never be able to say did
Speaker 4: the red come from the left side or the right side.
Speaker 4: You would take infinite data like it is like you
Speaker 4: would have to break a classical random number generator. I
Speaker 4: can show you a quantum algorithm that can do that deterministically.
Speaker 4: So where we're thinking is when the data appears to
Speaker 4: be completely unstructured or you looks essentially like a complete
Speaker 4: random number to the classical methods, there are quantum methods
Speaker 4: that can actually potentially find that structure.
Speaker 3: That's it for this episode of Smart Talks with IBM.
Speaker 3: If you haven't already, be sure to check out my
Speaker 3: conversation with IBM Chairman and CEO Arvind Krishna, and stay tuned.
Speaker 3: Another episode is coming soon. Smart Talks with IBM is
Speaker 3: produced by Matt Romano, Amy Gains, McQuaid, Trina Menino, and
Speaker 3: Jake Harper. Engineering by Bird Lawrence, Mastering by Sarah Buger,
Speaker 3: music by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and
Speaker 3: Sofia Derlon. Smart Talks with IBM is a production of
Speaker 3: Pushkin Industries and Ruby Studio at iHeartMedia to find more
Speaker 3: Pushkin podcasts. Listen on the iHeartRadio app, Apple Podcasts, or
Speaker 3: wherever you listen to podcasts. I'm Malcolm Godwell. This is
Speaker 3: a paid advertisement from IBM. The conversations on this podcast
Speaker 3: don't necessarily represent IBM's positions, strategies, or opinions.